Zahra Yaghoubi and Hassan Zarabadipour


  1. [1] M.H.A. Sidi, K. Hudha, Z.A. Kadir, and N.H. Amer, Modeling and path tracking control of a tracked mobile robot, 2018 IEEE 14th International Colloquium on Signal Proc. & Its Applications (CSPA), IEEE, Batu Feringghi, Malaysia, 2018, pp. 72–76.
  2. [2] H. Xiao, Z. Li, C. Yang, et al., Robust stabilization of a wheeled mobile robot using model predictive control based on neurodynamics optimization, IEEE Transactions on Industrial Electronics, 64(1), 2017, 505–516.
  3. [3] F.G. Rossomando, C. Soria, E.O. Freire, and R.O. Carelli, Sliding mode neuro-adaptive controller designed in discrete time for mobile robots, Mechatronic Systems and Control (formerly Control and Intelligent Systems), 46(2), 2018, 55–63.
  4. [4] F. Debbat and L. Adouane, Formation control and role assignment of autonomous mobile robots in unstructured environment, Control and Intelligent Systems, 44(2), 2016, 1–4.
  5. [5] M. Korayem, M. Yousefzadeh, and S. Manteghi, Dynamics and input–output feedback linearization control of a wheeled mobile cable-driven parallel robot, Multibody System Dynamics, 40(1), 2017, 55–73.
  6. [6] A. Brahmi, M. Saad, G. Gauthier, W.-H. Zhu, and J. Ghommam, Tracking control of mobile manipulator robot based on adaptive backstepping approach, International Journal of Digital Signals and Smart Systems, 1(3), 2017, 224–238.
  7. [7] L. Xue and G. Zhiyong, Adaptive sliding mode tracking control for nonholonomic wheeled mobile robots with finite time convergence, 2017 36th Chinese Control Conf. (CCC), IEEE, Dalian, China, 2017, 720–725.
  8. [8] L. Ding, S. Li, H. Gao, C. Chen, and Z. Deng, Adaptive partial reinforcement learning neural network-based tracking control for wheeled mobile robotic systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 1–12.
  9. [9] A.T. Azar, H.H. Ammar, and H. Mliki, Fuzzy logic controller ith color vision system tracking for mobile manipulator robot, International Conf. on Advanced Machine Learning Technologies and Applications, Springer, Cham, 2018, 138–146.
  10. [10] L. Ding, S. Li, Y.-J. Liu, H. Gao, C. Chen, and Z. Deng, Adaptive neural network-based tracking control for full-state constrained wheeled mobile robotic system, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(8), 2017, 2410–2419.
  11. [11] M. Duguleana and G. Mogan, Neural networks based reinforcement learning for mobile robots obstacle avoidance, Expert Systems with Applications, 62, 2016, 104–115.
  12. [12] S. S¸ahin and C. Guzeli¸s, Chaotification of real systems by dynamic state feedback, IEEE Antennas and Propagation Magazine, 52(6), 2010, 222–233.
  13. [13] G. Klancar, D. Matko, and S. Blazic, A control strategy for platoons of differential drive wheeled mobile robot, Robotics and Autonomous Systems, 59(2), 2011, 57–64.
  14. [14] S.-h. Kao, C.-c. Yang, C.-d. Shei, and G.-j. Sheu, Synchronization of chaotic gyros via a novel adaptive wrinkling-type terminal sliding mode control, Electrical Engineering and Automation: Proc. of the International Conf. on Electrical Engineering and Automation (EEA2016), Hong Kong, China, 2017, 1025–1032.
  15. [15] S. Peng and W. Shi, Adaptive fuzzy output feedback control of a nonholonomic wheeled mobile robot, IEEE Access, 6, 2018, 43414–43424.

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